AI and machine learning

We are building global collaborations, conducting world class research and helping to define the role of artificial intelligence (AI) and machine learning in the life sciences.

AI at EMBL-EBI

AI is being increasingly used to push the frontiers of life science research and bring people together across disciplines to facilitate more effective collaborations and open new avenues for exploration.

With the increasing amount of data produced in modern research, EMBL-EBI is fast becoming a key player in the field.

“The AlphaFold Database is an extraordinary effort in making biological data open on such a massive scale. This extremely valuable resource for the research community would not have been possible without the deep collaboration with the world-class team at EMBL.”

Demis Hassabis,

Nobel Prize laureate and co-founder of Google DeepMind

How is AI embedded across EMBL-EBI’s work?

Data resources

Our data experts curate, annotate, classify and enrich data to create the high quality data essential to develop AI algorithms, and use AI to streamline and accelerate their work.

Research

Our research teams develop new AI methods and use AI algorithms to conduct novel data analysis.

Training

Our Training Team create courses and workshops that help the scientific community exploit the potential of AI on high quality data.

Thought leadership and industry

Our thought leaders work closely with external experts in the field to help make AI algorithms and predicted data openly available to the scientific community.

Making AlphaFold predictions openly available: Janet Thornton and Milhay Varadi explain how this work will empower the scientists around the world.

Case study: AlphaFold uses open data and AI to discover the 3D protein universe

AI model forecasts disease risk decades in advance

How do EMBL-EBI data resources benefit from machine learning?

We store vast amounts of biological data and our researchers are experts in data management and curation. This makes us well equipped to critically evaluate the use of machine learning techniques to build links between resources, improve efficiency and close gaps in knowledge.

AlphaMissense data integrated into Ensembl, UniProt, DECIPHER and AlphaFold DB

AI annotations increase patent data in SureCheMBL

Machine learning to identify and prioritise drug targets

Meet EMBL-EBI’s AI and machine learning experts

AI has been used for many years in research, but as its practical applications grow, more of the challenges associated with using this technology start to emerge. Here, some of our thought leaders discuss the challenges faced when using AI and machine learning in their areas of expertise.

Jess Ewald on using machine learning to characterise chemical hazards

EMBL-EBI’s new research group is gathering insights from omics data and image-based cell profiling, to understand chemical hazards affecting people and the environment.

AI analysis for bioimages – what’s missing?

Lack of incentives and low adoption of metadata standards are limiting AI’s potential for bioimage analysis – a community initiative proposes solutions.

The AI revolution hinges on accessible training

Team Leader Sameer Velankar explains why researchers need accessible training to understand and leverage artificial intelligence in the life sciences.

Large language models for biomedical research

Discover BioChatter, an open-source large language model (LLM) framework designed for custom biomedical research.

AlphaFold: where will the next AI breakthrough come from?

EMBL’s Executive Director Ewan Birney reveals the key factors that enabled AlphaFold to change the world of biology.

Deciphering the data deluge using large language models

Learn from our experts how large language models are transforming scientific data curation.

AI training courses

The EMBL-EBI Training Team works with experts in the field of AI to deliver training that can help you build your own machine learning models and understand how to interpret the AI features in our data resources.